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The present invention relates to a method and system for use in treating a patient with any drug to optimize drug therapy and to prevent an adverse drug response. The present invention can utilize either drug levels or other surrogate markers to determine the effectiveness of the dosing regimen and, if necessary, to suggest a new more optimal drug dose.
When a patient begins taking any medication for a length of time, a titration of the amount of drug taken by the patient is necessary in order to achieve the optimal benefit of the drug, and at the same time to prevent any undesirable side effects that taking too much of the drug could produce. Thus, there is a continuous balance between taking enough drug in order to gain the benefits from that drug and at the same time not taking so much drug as to illicit a toxic event.
There is large inter-individual variability in the patient pharmacokinetics of drugs. What may be an appropriate drug dose for one individual, may be too much or too little for another. Prior to this invention a physician was required to estimate the correct drug dosage for a patient and then to experiment with that dosage, usually by trial and error, until the correct dosage was achieved. Likewise, the FDA labeling of a drug suggests dosages based on epidemiological studies and again does not account for inter-individual variability. Non-linear least squares modeling methods involve the use of large amounts of data relating to a general population in order to calculate a best fit. Much like linear regression models, this method cannot take into account the variability between people with the same population characteristics.
Bayesian analysis is another method used to relate drug dose to efficacy. This method employs large-scale population parameters to stratify a population in order to better characterize the individuals. This method does not take into account the changes that can occur within a person over time, and as a result cannot reliably estimate dosages.
Pharmacokinetic compartment modeling has had success with some drugs, but because the models are static and cannot adapt themselves to changes within a population or a patient, they are once again undesirable for dynamically determining drug dosages.
Expert systems have been developed using similar technology to predict drug dosages for immunosuppressant drugs (see, e.g., U.S. Pat. Nos. 5,365,948, 5,542,436 and 5,694,950). These algorithms, however, are not generic and only use immunosuppressant blood levels. Each algorithm is specific to an individual immunosuppressant drug. As it stands, these inventions cannot be applied to other drugs and do not have a non-linear feedback loop mechanism.
The term xe2x80x9cdrugxe2x80x9d as used herein includes, but is not limited to, substances which are conventionally called drugs, vaccines, serums, vitamin antagonists, medications, biological substances, and all substances derived from and/or related to the foregoing substances.
The present invention provides a method for calculating a new dose of a drug for a patient using said drug, comprising the steps of: accepting as a first input the patient""s current drug dose; accepting as a second input the maximum dose of the drug; accepting as a third input one or more numerical markers indicating a response of the patient; calculating said new dose, wherein said new dose is a function of said current dose minus the ratio of the change in numerical markers and the ratio of said current dose to said maximum dose plus the percent of individual patient response multiplied by a response factor; and said calculating step includes calculating said new dose based on the equation
NDD=CDDxe2x88x92{[((CDNMxe2x88x92DDNM)/CDNM)/(1+(CDD/HIGH))]xc3x97CDD}+LV 
and:
EDNM=[((CDDxe2x88x92PDD)/PDD)xc3x97(1+(PDD/HIGH))xc3x97PDNM]+PDNM 
and:
if CDNM less than DDNM, and EDNM greater than CDNM,
or if CDNM greater than DDNM, and EDNM less than CDNM,
then
LV=(RESPONSExc3x97CDD)xc3x97[(EDNMxe2x88x92CDNM)/CDNM]/[1.3{circumflex over ( )}(CDD/HIGH)], 
but if CDNM less than DDNM, and EDNM less than CDNM,
or if CDNM greater than DDNM, and EDNM greater than CDNM,
then
LV=xe2x88x921xc3x97(RESPONSExc3x97CDD)xc3x97[(CDNMxe2x88x92EDNM)/CDNM]/[1.3{circumflex over ( )}(CDD/HIGH)], 
wherein:
NDD=New Drug Dose
CDD=Current Drug Dose
CDNM=Current Drug Numerical Marker
DDNM=Desired Drug Numerical Marker
HIGH=The input parameter that is the high dose range for a particular drug
EDNM=Expected Drug Numerical Marker
PDD=Previous Drug Dose
PDNM=Previous Drug Numerical Marker
RESPONSE=Percent of total dose available for individualizing patient dose
1.3{circumflex over ( )}(CDD/HIGH)=1.3 raised to an exponent of (CDD/HIGH).
According to the present invention, patient dosing occurs through a cyclic series of events, depicted in flow chart form in FIG. 1. After an initial examination, an initial dose of a drug (therapeutic agent) is prescribed and administered by a physician for a patient. The initial dose is based on the FDA recommended dosage found on the drug label. The drug dose is further refined upon repeated dosing by the physician based on the patient""s response to the drug. Too much drug could cause the patient to experience toxic drug effects, and the drug dose would need to be reduced. Too little drug could cause the patient not to receive the benefit the drug therapy could offer, and the dosage would need to be increased.
This invention has at least two preferred embodiments; one which uses actual numerical surrogate markers to calculate a dose, and another embodiment that uses percentages as the numerical input for the surrogate markers.